Inquiry Response Mapping for Determining a Cybersecurity Risk Level of an Entity

1. A method for determining cybersecurity risk levels of entities by mapping responses to inquiry sets, the method comprising: modeling, by one or more processors, responses from an entity to one or more inquiries in a first inquiry set and a degree of similarity between the first inquiry set and a second inquiry set to predict a set of predicted responses from the entity to one or more inquiries in the second inquiry set, wherein modeling responses comprises: mapping the one or more inquiries in the first inquiry set to the one or more inquiries in the second inquiry set based on the degree of similarity, wherein the first inquiry set and the second inquiry set are parsed into a common format; calculating, by the one or more processors, a cybersecurity risk level of the entity using responses from the entity to inquiries in the first inquiry set and the set of predicted responses; generating, by the one or more processors, an alert based on a calculated overall cybersecurity risk score for the entity exceeding a threshold, the calculated overall cybersecurity risk score based at least in part on the cybersecurity risk level of the entity; determining a discrepancy between the mapped responses and the cybersecurity risk level of the entity; identifying, via a machine learning model, corrections to the discrepancy; and modifying, via the machine learning model, response mapping, wherein the machine learning model was trained to reduce future data conflicts based on feedback based training.

2. The method of claim 1, further comprising: mapping, by the one or more processors and utilizing the set of predicted responses, one or more responses from the entity to the one or more inquiries in the first inquiry set to the one or more inquiries in the second inquiry set.

3. The method of claim 1, further comprising: reading, by the one or more processors and from the first inquiry set of a plurality of inquiry sets, responses from the entity to the one or more inquiries in the first inquiry set.

4. The method of claim 1, further comprising: determining, by the one or more processors, the degree of similarity between the first inquiry set and the second inquiry set.

5. The method of claim 1, further comprising: comparing, by the one or more processors, the degree of similarity to a similarity threshold; and in response to a determination that the degree of similarity is greater than or equal to the similarity threshold, identifying, by the one or more processors, a match between the first inquiry set and the second inquiry set.

6. The method of claim 1, further comprising: comparing, by the one or more processors, the degree of similarity to a first similarity threshold; in response to a determination that the degree of similarity is less than or equal to the first similarity threshold, comparing, by the one or more processors, the degree of similarity to a second similarity threshold; and in response to a determination that the degree of similarity is greater than or equal to the second similarity threshold, generating, by the one or more processors, a prompt for user input to indicate whether the first inquiry set and the second inquiry set are a match.

7. The method of claim 1, further comprising: determining, by the one or more processors, a second degree of similarity between the first inquiry set and a third inquiry set; comparing, by the one or more processors, the second degree of similarity to a similarity threshold; and in response to a determination that the second degree of similarity is less than or equal to the similarity threshold, identifying, by the one or more processors, a mismatch between the first inquiry set and the third inquiry set.

8. The method of claim 1, wherein the degree of similarity is determined using a machine learning component or a machine learning algorithm.

9. The method of claim 1, wherein the responses to the one or more inquiries in the first inquiry set and the degree of similarity are modeled using a machine learning component or a machine learning algorithm.

10. The method of claim 1, further comprising: generating, by the one or more processors, a graphical user interface (GUI) comprising a first visual representation that includes, for the entity, an indication of a cybersecurity rating, an industry cybersecurity percentile ranking, an indication of a number of inquiry sets sent to the entity for response, an indication of a number of inquiry sets sent from the entity for response, one or more tags, or a combination thereof.

11. The method of claim 10, wherein the GUI further comprises a second visual representation that indicates information associated with each inquiry sent from another particular entity to the entity for response, information associated with each inquiry sent from the entity to the other particular entity for response, or both.

12. A system for populating data sets indicative of risk levels of multiple related entities, the system comprising: a memory; and one or more processors coupled to the memory, the one or more processors configured to: map, based on one or more inquiries in a first inquiry set and a degree of similarity between the first inquiry set and a second inquiry set, responses from an entity to the one or more inquiries in the first inquiry set to one or more inquiries in the second inquiry set to predict a set of predicted responses from the entity to one or more inquiries in the second inquiry set, wherein modeling responses comprises: mapping the one or more inquiries in the first inquiry set to the one or more inquiries in the second inquiry set based on the degree of similarity, wherein the first inquiry set and the second inquiry set are parsed into a common format; calculate a cybersecurity risk level of the entity utilizing responses from the entity to inquiries in the first inquiry set and the set of predicted responses; determining a discrepancy between the set of predicted responses and the cybersecurity risk level of the entity; identifying, via a machine learning model, corrections to the discrepancy; based on the cybersecurity risk level, provide a recommendation of one or more corrective actions to lower the cybersecurity risk level; and modifying, via the machine learning model, response mapping, wherein the machine learning model was trained to reduce future data conflicts based on feedback based training, wherein the feedback based training comprises adjusting a threshold for matches based on received feedback when a conflict is identified.

13. The system of claim 12, wherein the one or more processors are further configured to: calculate an overall cybersecurity risk score for the entity based, at least in part, on the cybersecurity risk level of the entity; and generate an alert based on the overall cybersecurity risk score exceeding a cybersecurity threshold.

14. The system of claim 12, wherein the one or more processors are further configured to: aggregate the cybersecurity risk level with cybersecurity risk levels of other entities in the same industry as the entity to calculate an aggregated cybersecurity risk level for the industry; and initiate presentation of a representation of the aggregated cybersecurity risk level for the industry.

15. The system of claim 12, wherein the one or more processors are further configured to: aggregate the cybersecurity risk level with cybersecurity risk levels of vendors of a plurality of vendors to calculate an aggregated cybersecurity risk level for vendors for the entity; and initiate presentation of a representation of the aggregated cybersecurity risk level for the vendors.

16. The system of claim 12, wherein: each inquiry set corresponds to a questionnaire; the entity is a vendor of a plurality of vendors for a company; or both.

17. A non-transitory computer-readable storage medium storing instructions that, when executed by one or more processors, cause the one or more processors to perform operations for mapping responses to inquiry sets related to cybersecurity, the operations comprising: modeling responses from an entity to one or more inquiries in a first inquiry set and a degree of similarity between the first inquiry set and a second inquiry set to predict a set of predicted responses from the entity to one or more inquiries in the second inquiry set, wherein modeling responses comprises: mapping the one or more inquiries in the first inquiry set to the one or more inquiries in the second inquiry set based on the degree of similarity, wherein the first inquiry set and the second inquiry set are parsed into a common format; mapping, utilizing the set of predicted responses, responses from the entity to the one or more inquiries in the first inquiry set to the one or more inquiries in the second inquiry set; and notifying the entity of a discrepancy between the mapped responses and a cybersecurity risk level of the entity for a cybersecurity category; identifying, via a machine learning model, corrections to the discrepancy; and modifying, via the machine learning model, response mapping, wherein the machine learning model was trained to reduce future data conflicts based on feedback based training.

18. The non-transitory computer-readable storage medium of claim 17, wherein the operations further comprise: receiving an upload of the first inquiry set; and initiating display of the set of predicted responses.

19. The non-transitory computer-readable storage medium of claim 17, wherein the operations further comprise: receiving an upload of a third inquiry set; reading, from the third inquiry set, one or more responses from the entity to one or more inquiries in the third inquiry set; and determining a degree of similarity between the third inquiry set and the second inquiry set.

20. The non-transitory computer-readable storage medium of claim 17, wherein the operations further comprise: determining an industry cybersecurity percentile ranking for the entity based on the cybersecurity risk level of the entity.